| | --- |
| | base_model: |
| | - Wan-AI/Wan2.1-VACE-14B |
| | - Wan-AI/Wan2.1-VACE-1.3B |
| | --- |
| | Combined and quantized models for WanVideo, originating from here: |
| |
|
| | https://huggingface.co/Wan-AI/ |
| |
|
| | Can be used with: https://github.com/kijai/ComfyUI-WanVideoWrapper and ComfyUI native WanVideo nodes. |
| |
|
| | Other model sources: |
| |
|
| | TinyVAE from https://github.com/madebyollin/taehv |
| |
|
| | SkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9 |
| |
|
| | WanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17 |
| |
|
| | --- |
| |
|
| | Lightx2v: |
| |
|
| | CausVid 14B: https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid |
| |
|
| | CFG and Step distill 14B: https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill |
| |
|
| | --- |
| |
|
| | CausVid 1.3B: https://huggingface.co/tianweiy/CausVid |
| |
|
| | AccVideo: https://huggingface.co/aejion/AccVideo-WanX-T2V-14B |
| |
|
| | Phantom: https://huggingface.co/bytedance-research/Phantom |
| |
|
| | ATI: https://huggingface.co/bytedance-research/ATI |
| |
|
| | MiniMaxRemover: https://huggingface.co/zibojia/minimax-remover |
| |
|
| | MAGREF: https://huggingface.co/MAGREF-Video/MAGREF |
| |
|
| | FantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking |
| |
|
| | MultiTalk: https://github.com/MeiGen-AI/MultiTalk |
| |
|
| | Anisora: https://huggingface.co/IndexTeam/Index-anisora/tree/main/14B |
| |
|
| | --- |
| | CausVid LoRAs are experimental extractions from the CausVid finetunes, the aim with them is to benefit from the distillation in CausVid, rather than any actual causal inference. |
| | --- |
| | v1 = direct extraction, has adverse effects on motion and introduces flashing artifact at full strength. |
| |
|
| | v1.5 = same as above, but without the first block which fixes the flashing at full strength. |
| |
|
| | v2 = further pruned version with only attention layers and no first block, fixes flashing and retains motion better, needs more steps and can also benefit from cfg. |
